Studentship Vacancies

PhD Studentship Vacancies

Applicants are required to develop a project proposal as part of the application process and should contact the named supervisor(s) for their topic of interest to get more information and guidance on developing their application. Visit our FAQs for more helpful information about our PhD studentships. Further details on the application process are available on the 'How to Apply' and 'Writing a PhD Proposal' pages.

The closing date for applications is: 09:00 (UK time) Monday, 5th February 2024.

The following list of topics, grouped by research area, are available this year.

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Area 1. Blockchains and Decentralised Systems

A blockchain is an open, decentralised and trustable system. Without the need for any central control or mediator, blockchains allow us to rethink applications in a decentralised way, providing a provenance protocol for sharing data across disparate semi-trusting organisations.

• Decentralised Online Media Commenting for 21st Century Digital Journalism

  Media and News companies are increasingly shutting down online commenting sections. This mostly stems from the lack of existing discussion platforms that can effectively respond to the requirements of civil, accountable discussion, including respect for anonymity and authenticity of people, facts and opinions. This PhD aims to study, design and develop a novel Digital Journalism discussion tool, which goes beyond these limitations and offers a viable alternative for media companies to re-open commenting to its readers. This technology will build on Artificial Intelligence and Decentralised Systems approaches to facilitate healthy discussions, promoting civilised free speech in online media.

  Supervisors: Prof Anna De Liddo

  Keywords: Online Media Commenting Digital Journalism Social Justice Anonymous Reputation Decentralised Systems Artificial Intelligence

  Skillset: Programming (Web development, Distributed Ledgers) Statistics User Studies/Research A passion for Digital Journalism

  Study mode: Full-time & Part-time

• Decentralised, Trusted Explainable AI Reasoning

  Over recent times concerns have been raised on how companies sometimes abuse personal user data within their internal AI reasoning systems. What if we could decentralise the reasoning and put it directly in the hands of users? Can we create a framework where automated reasoning can be carried out without the need for a central authority? How can we facilitate trust in automated reasoning processes and explain this clearly to non-computer scientists? This research is important for the development of more transparent, trustworthy AI systems that can be trusted to make sometimes life-changing decisions in complex, dynamic environments.

  Supervisors: Prof John Domingue and Dr Aisling Third

  Keywords: Knowledge Graphs Deep Learning Trust Blockchains Decentralised Ledgers

  Skillset: Software and web development Blockchain (e.g., Ethereum) Linked Data Knowledge Graphs Deep Learning

  Study mode: Full-time & Part-time

• Facilitating Trust in Collaborative AI and Human Ecosystems with Distributed Ledgers

  This PhD will focus on combining AI and blockchain technology to improve trust and cooperation between AI systems and humans in collaborative settings. The research aims to explore how distributed ledger technology can be used to enhance communication and coordination between AIs and humans, with the goal of creating trusted, efficient and effective collaborative ecosystems. The findings of this research could have important implications for a wide range of fields, including finance, supply chain management, and security. Ultimately, the goal is to provide a foundation to help AIs and humans work together more effectively, leading to better outcomes for all parties involved.

  Supervisors: Prof John Domingue, Dr Amel Bennaceur and Dr Aisling Third

  Keywords: Artificial Intelligence Autonomous Agents Trust Blockchains Decentralised Ledgers Linked Data Knowledge Graph

  Skillset: Software and web development Blockchain (e.g., Ethereum) Linked Data Autonomous Agents

  Study mode: Full-time & Part-time

• Intelligent Companions

  This PhD will focus on the technical challenges and potential applications of decentralised personalised individually-owned AI services running on personal devices. The candidate will explore questions, such as a) what the AI agents could learn with limited computational capabilities and user-specific data from individuals and households and b) how these agents would act as users' proxies and interact with available services and resources such as open knowledge bases and other centralised/specialised AI services. The candidate will develop new approaches to encoding long-term goals for conversational agents co-developed with their owner-users in real-life scenarios of lifelong wellbeing applications.

  Supervisors: Dr Alessio Antonini and Dr Iman Naja

  Keywords: Personal Computing Positive Computing Distributed Multi-Agent Systems Swarm Agents Data Ownership

  Skillset: Programming (mobile development, web development, graph databases) Multi-Agent Systems Conversational Agents (e.g., chatbots)

  Study mode: Full-time & Part-time

• Self-sovereign Valuable Personal Data

  Recent events with the acquisition of Twitter have encouraged people to explore decentralised and federated services such as Mastodon as an alternative, and technologies like self-sovereign identity and Solid personal data pods take the idea of a decentralised Web seriously. These architectures challenge current assumptions about trust in data, its contents, and how it is used. This PhD will investigate how personal data and trust are related in the context of decentralised data and identity technologies. Possible application areas include education, healthcare, social media, and equitable access to data infrastructure and value for marginalised groups. This PhD will build on existing work where we have created a framework (LinkChains) for handling personal and sensitive data which combines decentralised platforms with cryptographic and distributed ledger verification.

  Supervisors: Dr Aisling Third and Prof John Domingue

  Keywords: Identity Self-sovereignty Blockchain Decentralised Ledgers Linked Data Knowledge Graph Solid EDI

  Skillset: Software and web development Blockchain (e.g., Ethereum) Linked Data

  Study mode: Full-time & Part-time

Area 2. Computational Social Science

Computational Social Science research targets the enhancement of critical societal issues through the use of Artificial Intelligence solutions. This research aligns with the core values of The Open University, and has contributed to urgent and vital topics, such as misinformation detection, online radicalisation and extremism, crisis management, and climate change.

• Explainable AI for Neurodiverse Patients

  This PhD project focuses on advancing the realm of explainable AI by investigating the usefulness and accessibility of AI explanations to neurodiverse individuals, specifically patients. The research goal is to enhance the understanding and accessibility of AI decisions in the healthcare domain for this specific demographic. This PhD research will scrutinise existing AI algorithms to improve their explainability and adaptability to diverse cognitive styles within the neurodiverse spectrum. Simultaneously, it will investigate the human-computer interaction elements, aiming to create interfaces that neurodiverse individuals comprehend, trust, and engage with effectively. Through this interdisciplinary approach, the project seeks to contribute to the ethical and accessible development of AI, ensuring that the benefits of cutting-edge technologies are genuinely inclusive and empowering for all members of our diverse society.

  Supervisors: Dr Retno Larasati, Dr. Soraya Kouadri Mostefaoui and Dr Tracie Farrell

  Keywords: Explainable AI Artificial Intelligence for Equality, Diversity and Inclusion (AI4EDI) Accessible AI

  Skillset: Basics of Artificial Intelligence/Machine Learning (desired) Programming Qualitative Analysis Human-Computer Interaction Artificial Intelligence for Equality Diversity and Inclusion (AI4EDI)

  Study mode: Full-time & Part-time

Area 3. Data Science and Extended Intelligence

Data Science and Extended Intelligence go beyond efficient data infrastructure and engineering, it studies data empowered human processes that lead to smarter, fairer, more sustainable and equitable ways of living.

• Human AI Learning Through Dialogic Interfaces

  Set against recent AI advancements, this PhD research will explore the dynamics of collaborative dialogue between humans and AI in learning environments, guided by Bakhtin's dialogism (Bakhtin, 1984; Trausan-Matu et al., 2021). The research will focus on the interplay of language, encompassing textual and visual forms, and its role in facilitating group cognition, meaning negotiation, and knowledge building within these environments. The emphasis is on designing and evaluating innovative Human-AI collaborative frameworks. This PhD is ideal for those intrigued by AI, collaboration, and philosophy, and provides an opportunity to shape a developing area, merging technology and theory to augment learning processes in online communities such as wikis, social media websites, and citizen science platforms.

  Supervisors: Dr Nirwan Sharma, Prof Advaith Siddharthan and Prof Stefan Rueger

  Keywords: Collaborative Learning Human-Computer Interaction Dialogism Artificial Intelligence Citizen Science Machine Learning

  Skillset: Basic programming skills Interest in AI/Machine Learning Mixed methods research

  Study mode: Full-time & Part-time

• Data Integration with Knowledge Graphs and AI

  Integrated knowledge is key in industry domains such as smart cities, education, biomedicine, web science, and cultural heritage. However, defining and encoding data transformations constitutes a significant bottleneck, hindering the ability of large organisations to make sense of vast heterogeneous data effectively. The project aims at designing novel methods for building Knowledge Graphs (KG) integrating data from heterogeneous sources, combining symbolic (mappings, rules, plans) and subsymbolic AI such as machine learning and Large Language Models (LLM). The candidate will have strong software engineering skills and an interest in semantic technologies (RDF, SPARQL, OWL, SHACL) and will contribute to addressing issues in KG construction and application such as (1) automating KG generation from structured and semi-structured data sources; (2) accessing non-RDF resources with SPARQL (Virtual Knowledge Graphs); (3) improving the usability of KG construction systems for non-expert users.

  Supervisors: Dr Enrico Daga and Dr Paul Mulholland

  Keywords: Data Integration Semantic Web

  Skillset: (required) Programming, Basics of Artificial Intelligence Strong interest in Semantic Web technologies

  Study mode: Full-time & Part-time

• Open Research Graph

  This project will develop novel AI methods for knowledge graph generation from large quantities of research texts. The student will work with the world's largest and continuously growing dataset of full text open access research papers, hosted by the research group at core.ac.uk, which has over 30 million monthly active users. The student will be able to test the developed technology in production in a real-world use case in cooperation with several companies.

  Supervisors: Dr Petr Knoth and Dr David Pride

  Keywords: Knowledge Graph Research Graph Machine Learning Artificial Intelligence Big Data Open Science Open Access

  Skillset: Natural Language Processing Machine Learning Information Retrieval Data Mining Big Data

  Study mode: Full-time & Part-time

• Responsible Use of AI in Recommender Systems for Finding Experts

  The objective of this project is to develop innovative AI methods for identifying experts possessing highly specific knowledge and skills. The process of finding experts with relevant skills is currently too resource intensive and prone to biases. The student will work with the world's largest and continuously growing dataset of full text open access research papers, hosted by the research group at core.ac.uk and which has over 30 million monthly active users. The student will be able to test the developed technology in production in a real-world use case with scientific publishers / funders.

  Supervisors: Dr David Pride and Dr Petr Knoth

  Keywords: Machine Learning Artificial Intelligence Big Data Open Science Open Access

  Skillset: Natural Language Processing Machine Learning Information Retrieval Data Mining Big Data

  Study mode: Full-time & Part-time

• Integrating Large Language Models and Knowledge Graphs for Advancing Scientific Discovery

  This PhD research focuses on merging the capabilities of Large Language Models (LLMs) and Knowledge Graphs (KGs) to support scientific research. These technologies can assist researchers, students, editors, and funding bodies in navigating scientific literature, exploring research trends, extracting relevant information, assessing research impact, and even formulating new scientific hypotheses. The main objective of this project is to integrate LLMs and KGs for facilitating scientific discovery and providing robust assistance to the scientific community. The project envisions developing innovative tools that not only streamline the research process but also foster groundbreaking scientific discoveries.

  Supervisors: Dr Angelo Salatino, Dr Francesco Osborne and Prof Enrico Motta

  Keywords: Large Language Models Knowledge Graphs Scientometrics Scholarly Analytics Scholarly Data Semantic Web Science of Science

  Skillset: Interest/expertise in Science Data Science Computer Programming Natural Language Processing Data Integration

• Developing Trustworthy and Transparent Large Language Models

  This PhD will explore innovative methods to enhance the reliability and transparency of Large Language Models (LLMs). It addresses critical limitations in current LLMs, such as biases, hallucinations, and the challenge of processing less frequent data ('long tail' problem). The project will investigate novel approaches for incorporating into LLMs verifiable knowledge sources, such as web content and knowledge graphs. The focus is on creating a beneficial cycle where LLMs extract information from repositories of textual data, encode them into knowledge graphs that humans can inspect and correct, and then use this refined knowledge as context to mitigate hallucinations and produce verifiable outputs. The ultimate goal is to develop a new generation of LLMs that are not only trustworthy but also verifiable, making them suitable for critical applications in business, science, and governance.

  Supervisors: Dr Francesco Osborne, Dr Angelo Salatino and Prof Enrico Motta

  Keywords: Large Language Models Knowledge Graphs Scientometrics Scholarly Analytics Scholarly Data Semantic Web Science of Science

  Skillset: Interest/expertise in Science Data Science Computer Programming Natural Language Processing Data Integration

Area 4. New Media in Society

New Media and Society research aims at going beyond the study of Computing and ICT from a technology perspective, and looks at improving our understating human values and the impact of technology innovations on people's lives and their communities. This research particularly looks at ways to use new media to promote social justice and tackle complex societal challenges of inclusion and disadvantage.

• Artificial Intelligence for Large-Scale Analysis of the News Dynamics

  This research will design AI techniques for modelling the news agenda at scale, focusing on understanding the dynamics of topics (what subjects are covered in the news) and viewpoints (what perspectives are expressed for a given topic). Because viewpoints are characterised as aggregations of "congruent claims", a key research challenge will be to develop effective AI techniques able to abstract the meaningful alternative political viewpoints that emerge from the plethora of claims present in the news. In the course of this project, the student will develop novel solutions based on machine learning and natural language processing techniques, taking advantage of the latest generation of Large Language Models.

  Supervisors: Prof Enrico Motta, Dr Enrico Daga, Dr Francesco Osborne and Dr Angelo Salatino

  Keywords: News Analytics Argument Extraction Viewpoint Extraction Topic Classification Data Science Deep Learning Knowledge Graphs Natural Language Processing Information Extraction Large Language Models

  Skillset: Programming Machine Learning Natural Language Processing Knowledge Graphs Interest/expertise in news and media

  Study mode: Full-time & Part-time

• Citizen Curation

  Citizen Curation can be defined as individuals and groups from outside the museum profession engaging in curatorial activities to communicate their own ideas, experiences, perspectives and stories. Increasingly, museums support visitors in developing and sharing their own interpretations of artworks. This can be particularly valuable for minoritized groups whose concerns and interests may not be reflected in the interpretations offered by the museum. There is great potential for new technology to support citizens in interpreting artworks and curating their own responses, for example, novel interfaces to support citizens in sharing and discussing their own interpretations or the use of AI technologies to assist in generating alternative interpretations of the same artwork. The candidate will develop and evaluate novel technology to assist in Citizen Curation.

  Supervisors: Dr Paul Mulholland and Dr Enrico Daga

  Keywords: Citizen Curation Museum Interpretation

  Skillset: Knowledge of HCI software development or AI Interest in museum engagement and new technologies

  Study mode: Full-time & Part-time

• Emerging Technologies in Higher Education

  The use of technology to enhance the learning experience is a vibrant research area transforming higher education. This PhD will investigate the potential of new and emerging technologies in education, for example exploring AI solutions for facilitating personalised learning and self-regulated learning, supporting lifelong learners in their personal and professional progression, exploring the use of new forms of accreditation such as micro-credentials, etc. This PhD will build upon the results of recent European-funded research projects, including QualiChain and the OpenLang Network.

  Supervisors: Dr Alexander Mikroyannidis and Dr Trevor Collins

  Keywords: Technology-Enhanced Learning Educational Technology Artificial Intelligence Personalised Learning Self-Regulated Learning Lifelong Learning Micro-Credentials

  Skillset: Education skills/interest Technology skills/interest Evaluation studies Research Methods Written and oral communication

  Study mode: Full-time & Part-time

• The Role of Technology in Fieldwork Education

  Within the geosciences, biosciences and environmental sciences fieldwork is seen as a signature pedagogy that provides opportunities for students to put their learning into use, develops students' collaboration and team working skills, and fosters students' sense of belonging within their discipline. This PhD will investigate the role technology plays in fieldwork education. Depending on the applicants' interests and experience, the project may evaluate existing practice or develop and trial new applications. Potential areas of interest include: the use of technology to enhance in-field teaching; the affordances and limitations of virtual field experiences; and/or the use of web broadcasting and streaming technologies to provide remote access to field sites and field scientists.

  Supervisors: Dr Trevor Collins, Dr Sarah Davies and Dr Karen Kear

  Keywords: Educational Technology Experiential Learning Fieldwork Education Technology-Enhanced Learning

  Skillset: Evaluation studies Research Methods Technology development or application Written and oral communication

  Study mode: Full-time & Part-time

• Public Perceptions of Artificial Intelligence in Low- and Middle-Income Countries

  Public perception of AI, its risks and benefits, is continuously shaped by media representations, research literature and influential figures from wealthier, more industrialised nations. As more low and middle-income countries continue to be impacted by economic, political and technological global systems and embrace AI, we need to understand the specific impacts implicit in real-life applications in different regions of the world. Who are the users and beneficiaries of AI? What are the causal impacts and effectiveness of AI for Social Good (AI4SG) programs, and can trust and fairness be integrated to involve communities that are not usually represented? This PhD will evaluate AI-related programs or interventions and explore methods to consult and engage the general public. The project aims to understand the missing perspectives of AI workers and identify critical issues and concerns of stakeholders and the general public.

  Supervisors: Dr Venetia Brown, Dr Tracie Farrell and Prof Anna DeLiddo

  Keywords: Artificial Intelligence AI for Social Good Stakeholder Participation Global Systems

  Skillset: Qualitative and mixed methods research evaluation studies participatory design interdisciplinary research

  Study mode: Full-time & Part-time